Analysis of Cloud-Resolving Model Simulations for Scale Dependence of Convective Momentum Transport.

We use 3-D cloud-resolving model (CRM) simulations of two mesoscale convective systems at mid-latitudes and a simple statistical ensemble method to diagnose the scale dependency of convective momentum transport (CMT) and CMT-related properties, and evaluate a parameterization scheme for convection-induced pressure gradient (CIPG) developed by Gregory et al. (GKI97). GKI97 relates CIPG to a constant coefficient multiplied by mass flux and vertical mean wind shear. CRM results show that mass fluxes and CMT exhibit strong scale dependency in temporal evolution and vertical structure. The prevalent understandings of CMT characteristics in terms of upgradient/downgradient transport are applicable to updrafts but not downdrafts across a wide range of grid spacings (4-512 km). For the small-to-median grid spacings (4~64 km), GKI97 reproduces some aspects of CIPG scale dependency except for underestimating the variations of CIPG as grid spacing decreases. However, for large grid spacings (128~512 km), GKI97 might even less adequately parameterize CIPG because it omits the contribution from either the nonlinear shear or buoyancy forcings. Further diagnosis of CRM results suggests that inclusion of nonlinear shear forcing in GKI97 is needed for the large grid spacings, and use of the three-updraft and one downdraft approach proposed in an earlier study may help a modified GKI97 capture more variations of CIPG as grid spacing decreases for the small-to-median grid spacings. Further, the optimal coefficients used in GKI97 seems insensitive to grid spacings, but they might be different for updrafts and downdrafts, for different MCS types, and for zonal and meridional components.

Differences in the Hydrological Cycle and Sensitivity Between Multiscale Modeling Frameworks with and without a Higher-order Turbulence Closure.

Current conventional global climate models (GCMs) produce a weak increase in global mean precipitation with anthropogenic warming in comparison with the lower-tropospheric moisture increases. The motive of this study is to understand the differences in the hydrological sensitivity between two multiscale modeling frameworks (MMFs) that arise from the different treatments of turbulence and low clouds in order to aid to the understanding of the model spread among conventional GCMs. We compare the hydrological sensitivity and its energetic constraint from MMFs with (SPCAM-IPHOC) or without (SPCAM) an advanced higher-order turbulence closure. SPCAM-IPHOC simulates higher global hydrological sensitivity for the slow response but lower sensitivity for the fast response than SPCAM. Their differences are comparable to the spreads of conventional GCMs. The higher sensitivity in SPCAM-IPHOC is associated with the higher ratio of the changes in latent heating to those in net atmospheric radiative cooling, which is further related to a stronger decrease in the Bowen ratio with warming than in SPCAM. The higher sensitivity of cloud radiative cooling resulting from the lack of low clouds in SPCAM is another major factor in contributing to the lower precipitation sensitivity. The two MMFs differ greatly in the hydrological sensitivity over the tropical lands, where the simulated sensitivity of surface sensible heat fluxes to surface warming and CO2 increase in SPCAM-IPHOC is weaker than in SPCAM. The difference in divergences of dry static energy flux simulated by the two MMFs also contributes to the difference in land precipitation sensitivity between the two models.

Evaluation of a general circulation model by the CERES Flux-by-cloud type simulator.

In this work, we use the Clouds and the Earth's Radiant Energy System (CERES) FluxByCloudTyp data product, which calculates TOA shortwave and longwave fluxes for cloud categories defined by cloud optical depth (τ) and cloud top pressure (pc ), to evaluate the HadGEM2-A model with a simulator. The CERES Flux-by-cloud type simulator is comprised of a cloud generator that produces subcolumns with profiles of binary cloud fraction, a cloud property simulator that determines the (τ, pc ) cloud type for each subcolumn, and a radiative transfer model that calculates TOA fluxes. The identification of duplicate atmospheric profiles reduces the number of radiative transfer calculations required by approximately 97.6%. In the Southern Great Plains region in JFD (January, February, and December) 2008, the simulator shows that simulated cloud tops are higher in altitude than observed, but also have higher values of OLR than observed, leading to a compensating error that results in an average value of OLR that is close to observed. When the simulator is applied to the Southeast Pacific stratocumulus region in JJA 2008, the simulated cloud tops are primarily low in altitude; however, the clouds tend to be less numerous, and have higher optical depths than are observed. In addition to the increase in albedo that comes from having too many clouds with higher optical depth, the HadGEM2-A albedo is higher than observed for those cloud types that occur most frequently. The simulator is also applied to the entire 60° N - 60° S region, and it is found that there are fewer clouds than observed for most cloud types, but there are also higher albedos for most cloud types, which represents a compensating error in terms of the shortwave radiative budget.

Covariance between Arctic sea ice and clouds within atmospheric state regimes at the satellite footprint level.

Understanding the cloud response to sea ice change is necessary for modeling Arctic climate. Previous work has primarily addressed this problem from the interannual variability perspective. This paper provides a refined perspective of sea ice-cloud relationship in the Arctic using a satellite footprint-level quantification of the covariance between sea ice and Arctic low cloud properties from NASA A-Train active remote sensing data. The covariances between Arctic low cloud properties and sea ice concentration are quantified by first partitioning each footprint into four atmospheric regimes defined using thresholds of lower tropospheric stability and midtropospheric vertical velocity. Significant regional variability in the cloud properties is found within the atmospheric regimes indicating that the regimes do not completely account for the influence of meteorology. Regional anomalies are used to account for the remaining meteorological influence on clouds. After accounting for meteorological regime and regional influences, a statistically significant but weak covariance between cloud properties and sea ice is found in each season for at least one atmospheric regime. Smaller average cloud fraction and liquid water are found within footprints with more sea ice. The largest-magnitude cloud-sea ice covariance occurs between 500 m and 1.2 km when the lower tropospheric stability is between 16 and 24 K. The covariance between low cloud properties and sea ice is found to be largest in fall and is accompanied by significant changes in boundary layer temperature structure where larger average near-surface static stability is found at larger sea ice concentrations.